Studies of COVID-19 Outbreak Control Using
Agent-Based Modeling
Shaoping Xiao
Ruicheng Liu
Department of Mechanical Engineering
University of Iowa
3131 Seamans Center
Iowa City, IA 52242, USA
Abstract
An agent-based model was developed to study outbreaks and outbreak control for COVID-19, mainly in urban communities. Rules for people’s interactions and virus infectiousness were derived based on previous sociology studies and recently published data-driven analyses of COVID-19 epidemics. The calculated basic reproduction number of epidemics from the developed model coincided with reported values. There were three control measures considered in this paper: social distancing, self-quarantine and community quarantine. Each control measure was assessed individually at first. Later on, an artificial neural network was used to study the effects of different combinations of control measures. To help quantify the impacts of self-quarantine and community quarantine on outbreak control, both were scaled respectively. The results showed that self-quarantine was more effective than the others, but any individual control measure was ineffective in controlling outbreaks in urban communities. The results also showed that a high level of self-quarantine and general community quarantine, assisted with social distancing, would be recommended for outbreak control.
Keywords: agent-based modeling; COVID-19; control measures; neural network
Cite this publication as:
S. Xiao and R. Liu, “Studies of COVID-19 Outbreak Control Using Agent-Based Modeling,” Complex Systems, 30(3), 2021 pp. 297–321.
https://doi.org/10.25088/ComplexSystems.30.3.297
